چکیده مقاله
Predicting the probability that a football shot will result in a goal known as Expected Goals xG is a critical task for performance analysis and decision support in modern football analytics Given the limitations of the available dataset and the absence of key variables, we devoted significant effort to feature engineering In this project, we develop a regression based xG model by combining shot specific features e g , distance, angle, shot type , contextual variables e g , game state, player status We cleaned and normalized the dataset, then split it 80/20 into training and testing sets We performed hyperparameter tuning for each model using a k fold cross validation approach Model performance is assessed via mean squared error MSE , and R squared R² on test data Also, we used a new metric to check xG prediction interval Experimental results demonstrate that tree based models have the best performance The top performing regressor was Bagging, which achieved to R² of 0 878
کلیدواژهها
نویسندگان
Amirali Khatib
Department of Industrial and Systems Engineering, Faculty of Engineering, Ferdowsi University of Mashhad
Amirali Bagherzadeh Biouky
Department of Industrial and Systems Engineering, Faculty of Engineering, Ferdowsi University of Mashhad
Alireza Shadman
Department of Industrial and Systems Engineering, Faculty of Engineering, Ferdowsi University of Mashhad
شیوه ارجاع
Khatib, Amirali and Bagherzadeh Biouky, Amirali and Shadman, Alireza,1404,Regression-Based Modeling of Expected Goals in Football Using Hudl-Stats Bomb Event Data,11th International Conference on Industrial and System Engineering,Mashhad
ارائهشده در
مجموعه مقالات یازدهمین کنفرانس بین المللی مهندسی صنایع و سیستم ها2 مهر 1404 · مشهد